Exploring multi-agent systems with LangGraph, LangChain, and a vector database. The goal is to create a retrieval-augmented generation (RAG) pipeline that leverages Llama as the large language model (LLM) agent to intelligently answer user queries. The system dynamically routes queries to either a local corpus (powered by AstraDB as the vector database) or a Wikipedia agent for external knowledge retrieval, all orchestrated through a simple LangGraph setup.
For this iteration, I used a straightforward AstraDB instance as the vector datastore. In the next phase, I plan to replace AstraDB with OpenSearch, while keeping the overall concept intact.
Excited to see where this journey leads—feel free to share your thoughts or comments! 🚀
- Role: Facilitates interaction with large language models (LLMs) and tool integrations.
- Applications:
- Decision-making for query routing.
- Retrieval from external tools like Astra DB and Wikipedia.
- Producing structured outputs for downstream processes.
- Role: Models and executes workflows as a directed state graph.
- Applications:
- Node and edge definitions for workflow orchestration.
- Visualizes workflows and compiles them for execution.
- Enables dynamic routing based on query type.
- Role: Acts as the vector database for domain-specific document retrieval.
- Applications:
- Stores pre-processed, vectorized documents for fast retrieval.
- Serves as the knowledge base for specialized queries.
- Role: Provides real-time access to external general knowledge.
- Applications:
- Answers general or non-domain-specific queries.
Automatically route customer questions to the correct knowledge base, providing faster and more accurate responses.
Offer users a hybrid search experience by combining structured domain knowledge with real-time external sources.
Enable AI systems to adaptively handle queries, ensuring domain expertise while maintaining flexibility for general questions.
Provide students and professionals with relevant knowledge by integrating diverse knowledge sources dynamically.
- Input: A natural language query from the user.
- Logic:
- Queries are analyzed by LangChain's LLM.
- Decision function (
route_question) determines whether to:- Fetch general knowledge from Wikipedia.
- Retrieve domain-specific documents from Astra DB.
- Framework: Designed and executed with LangGraph.
- Nodes:
route_question: Routes queries to appropriate nodes.wiki_search: Handles real-time external lookups from Wikipedia.astra_db_retrieve: Retrieves documents from Astra DB for specialized queries.
- Edges:
- Conditional routing using
route_questiondetermines the path based on the query context. - Terminal edges mark the completion of the workflow.
- Conditional routing using
- Start:
- User submits a question.
- Decision Point (
route_question):- Evaluates the query and routes it to the appropriate node:
wiki_searchfor general knowledge.astra_db_retrievefor domain-specific questions.
- Evaluates the query and routes it to the appropriate node:
- Processing Nodes:
wiki_search: Executes a Wikipedia search for external information.astra_db_retrieve: Retrieves vectorized documents from Astra DB.
- Completion:
- Outputs the retrieved or generated response.
- LangChain's LLM analyzes queries and outputs routing decisions.
- Decisions are implemented through LangGraph’s conditional edges.
- Astra DB:
- Vectorized storage and retrieval for domain-specific content.
- Ensures fast and accurate results for specialized queries.
- Wikipedia:
- Real-time search integration for broad, general-purpose questions.
- Node Definitions:
- Encapsulate specific functions like
route_question,wiki_search, andastra_db_retrieve.
- Encapsulate specific functions like
- Dynamic Transitions:
- Conditional edges enable dynamic routing based on query type.
- Visualization:
- The graph structure can be visualized for better debugging and understanding.